3 citations · 12 across the 18 of their papers we have counts for
22 papers
OneVoice: An Intermediate Representation for Agentic Speech Pipelines
Vipul Charugundla, Dancheng Liu, Jinjun Xiong
Agentic speech systems must exchange more than text, including speaker identity, timing, phonetic information, and behavioral annotations. Yet these signals are often produced in i…
PolySim: Deterministic Polynomial Surrogates for Cross-Modal Retrieval on CiM
Xinzhao Li, Charles Power, Pengyu Ren +10
Cross-modal retrieval on edge devices benefits from probabilistic embeddings that capture semantic uncertainty, but deploying them on compute-in-memory (CiM) hardware remains an op…
CHILDES-Aligned: A Curated Children's Speech Dataset via Multi-Model Timestamp Ensembling
Haolong Zheng, Yuanzhuo Hu, Xinyu Liang +7
CHILDES is a large-scale child speech corpus containing long-form recordings of naturalistic child-adult interactions, making it a valuable resource for studying child speech and l…
KidSpeak: A General Multi-purpose LLM for Kids' Speech Recognition and Screening
Rohan Sharma, Dancheng Liu, Jingchen Sun +4
With the rapid advancement of conversational and diffusion-based AI, there is a growing adoption of AI in educational services, ranging from grading and assessment tools to persona…
Towards Pretraining Robust ASR Foundation Model with Acoustic-Aware Data Augmentation
Dancheng Liu, Amir Nassereldine, Chenhui Xu +1
Whisper's robust performance in automatic speech recognition (ASR) is often attributed to its massive 680k-hour training set, an impractical scale for most researchers. In this wor…
FP64 is All You Need: Rethinking Failure Modes in Physics-Informed Neural Networks
Chenhui Xu, Dancheng Liu, Amir Nassereldine +1
Physics Informed Neural Networks (PINNs) often exhibit failure modes in which the PDE residual loss converges while the solution error stays large, a phenomenon traditionally blame…
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability
Chenhui Xu, Dancheng Liu, Jiajie Li +3
Recent advancements in cognitive science and multi-round reasoning techniques for Large Language Models (LLMs) suggest that iterative thinking processes improve problem-solving per…
Driving Through Uncertainty: Risk-Averse Control with LLM Commonsense for Autonomous Driving under Perception Deficits
Yuting Hu, Chenhui Xu, Ruiyang Qin +4
Partial perception deficits can compromise autonomous vehicle safety by disrupting environmental understanding. Existing protocols typically default to entirely risk-avoidant actio…
Sub-Sequential Physics-Informed Learning with State Space Model
Chenhui Xu, Dancheng Liu, Yuting Hu +4
Physics-Informed Neural Networks (PINNs) are a kind of deep-learning-based numerical solvers for partial differential equations (PDEs). Existing PINNs often suffer from failure mod…
Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data
Jiajie Li, Brian R Quaranto, Chenhui Xu +5
We present RASO, a foundation model designed to Recognize Any Surgical Object, offering robust open-set recognition capabilities across a broad range of surgical procedures and obj…
NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs
Ruiyang Qin, Pengyu Ren, Zheyu Yan +7
Large Language Models (LLMs) deployed on edge devices, known as edge LLMs, need to continuously fine-tune their model parameters from user-generated data under limited resource con…
Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge
Ruiyang Qin, Dancheng Liu, Gelei Xu +7
The combination of Large Language Models (LLM) and Automatic Speech Recognition (ASR), when deployed on edge devices (called edge ASR-LLM), can serve as a powerful personalized ass…
Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges
Dancheng Liu, Jason Yang, Ishan Albrecht-Buehler +5
Speech is a fundamental aspect of human life, crucial not only for communication but also for cognitive, social, and academic development. Children with speech disorders (SD) face…
Towards Precision Characterization of Communication Disorders using Models of Perceived Pragmatic Similarity
Nigel G. Ward, Andres Segura, Georgina Bugarini +4
The diagnosis and treatment of individuals with communication disorders offers many opportunities for the application of speech technology, but research so far has not adequately c…
Automating Intervention Discovery from Scientific Literature: A Progressive Ontology Prompting and Dual-LLM Framework
Yuting Hu, Dancheng Liu, Qingyun Wang +5
Identifying effective interventions from the scientific literature is challenging due to the high volume of publications, specialized terminology, and inconsistent reporting format…
FASA: a Flexible and Automatic Speech Aligner for Extracting High-quality Aligned Children Speech Data
Dancheng Liu, Jinjun Xiong
Automatic Speech Recognition (ASR) for adults' speeches has made significant progress by employing deep neural network (DNN) models recently, but improvement in children's speech i…
Large Language Models have Intrinsic Self-Correction Ability
Dancheng Liu, Amir Nassereldine, Ziming Yang +8
Large language models (LLMs) have attracted significant attention for their exceptional abilities in various natural language processing tasks, but they suffer from hallucinations…
PI-Whisper: Designing an Adaptive and Incremental Automatic Speech Recognition System for Edge Devices
Amir Nassereldine, Dancheng Liu, Chenhui Xu +3
Edge-based automatic speech recognition (ASR) technologies are increasingly prevalent in the development of intelligent and personalized assistants. However, resource-constrained A…
Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices
Ruiyang Qin, Dancheng Liu, Chenhui Xu +9
The scaling laws have become the de facto guidelines for designing large language models (LLMs), but they were studied under the assumption of unlimited computing resources for bot…
Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures
Ruiyang Qin, Zheyu Yan, Dewen Zeng +8
Large Language Models (LLMs) deployed on edge devices learn through fine-tuning and updating a certain portion of their parameters. Although such learning methods can be optimized…
From Keyboard to Chatbot: An AI-powered Integration Platform with Large-Language Models for Teaching Computational Thinking for Young Children
Changjae Lee, Jinjun Xiong
Teaching programming in early childhood (4-9) to enhance computational thinking has gained popularity in the recent movement of computer science for all. However, current practices…
Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemble
Dancheng Liu, Chenhui Xu, Jiajie Li +2
For collaborative inference through a cloud computing platform, it is sometimes essential for the client to shield its sensitive information from the cloud provider. In this paper,…